CT (computed tomography) imaging systems can be used by radiologists to examine the blood vessels of the head and neck including degree of stenosis (narrowing) or aneurisms. Radiologists analyze the vessels in two scenarios. In one scenario each vessel is examined separately, traversing along the vessel and checking for aneurisms or stenosis. The second method is by using a MIP (Maximum Intensity Projection) view of the head and examining the overall structure of the blood vessels.
For the first scenario, a method for finding the centerline of each vessel is needed, while for the second scenario, it is desirable to have a view of the head without the intervening skull so as to get a good view of the various vessels. To implement the above two scenarios, segmentation algorithms for blood vessels, the brain, and the skull need to be developed. Once these are in place a radiologist can choose to display the blood vessels only with the skull and brain effectively removed from the image. The segmentation process as it relates to CT images involves using computational algorithms to identify parts and systems within the scanned human body. Segmented items can, for example, be highlighted, labeled or removed from the image.
Identification/segmentation of the head-neck arteries in a CT image allows a radiologist to examine each of the blood vessels in a panoramic view and to traverse along the vessel showing cross sectional views of the vessels that allow accurate measurements of the diameter and cross sectional area of the arteries as well as various aneurism and stenosis measurements.
It is noted that determination of the blood vessels versus other unrelated elements in the CT image is generally not a trivial task. In many cases parts of the blood vessel are missing from the scan, have non-uniform CT numbers (Hounsfield numbers) and/or the edges of the blood vessels are not clear. Exemplary reasons for this are:
a) Even if the blood vessel is imaged using an intravenous radiocontrast agent, this material may not be uniformly distributed along the blood vessel;
b) Partial volume effects, especially near bones;
c) Nearby tissue with similar absorption, mainly various bones that have similar HU values;
d) Narrowing, splitting and/or other geometrical properties of vessels;
e) Nearby vessels may appear to meet and merge and then diverge;
g) Various effects may cause a vessel to appear to include loops;
h) Occlusions;
i) Some slices may be scanned before the injection of the contrast agent, while other slices are scanned after the injection;
j) Noise.
Prior art segmentation tools have generally required at least some manual user input, such as identifying the starting point and ending points of the neck arteries, and segmenting each image can therefore require a lot of time by skilled professionals, making it impractical to routinely segment large numbers of medical images.
It is therefore desirable to provide a method for segmentation of the head-neck arteries that is fully automated.